机器人视觉数据压缩的痛点终于有了兼顾效率与兼容性的方案——SEAOTTER在200:1压缩比下比AVIF更快更准，做云机器人或边缘计算的团队可以直接用开源代码试试。
机器人系统常面临高分辨率视觉数据带宽和计算资源受限的问题，传统JPEG/MPEG编码器效率低，而AV1/AVIF等新编码器编码成本高且需专用硬件。SEAOTTER提出一种结合传感器嵌入式自编码器与一次性转码的压缩框架，在保持与JPEG基础设施兼容的同时，实现200:1压缩比下比AVIF快7倍编码、3.5倍解码，ImageNet top-1准确率提升8%。该方法通过可学习的JPEG颜色和量化变换，支持通用和任务感知的转码管道，适用于云机器人场景。代码已开源。
SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction
In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware. Yet, limited bandwidth and on-device compute resources prevent full utilization when transmitted via conventional codecs like JPEG/MPEG. Newer codecs, like AV1/AVIF, improve the rate-distortion trade-off, but demand far more resources for encoding, impractical without custom ASICs. Recent asymmetric autoencoders deliver high quality under extreme power and bandwidth constraints, but add prohibitive decoding cost and use bespoke formats that ignore decades of infrastructure built around standards like JPEG. To address these limitations, we introduce a compression framework for cloud robotics based on a Sensor Embedded Autoencoder paired with a One-Time Transcode for Efficient Reconstruction (SEAOTTER). Because the sensor, cloud, and consumer stages face very different power and bandwidth budgets, SEAOTTER combines the compactness of a learned latent with the broad usability of a standard JPEG file. Since naive transcoding degrades performance, we propose a learnable JPEG color and quantization transform that enables increased accuracy for global, dense, and vision-language-based perception. Using SEAOTTER, we train both general-purpose and task-aware transcoding pipelines for a pre-trained, frozen encoder. At a compression ratio of 200:1 and compared to AVIF, we observe 7 times faster encoding, 3.5 times faster decoding, and +8% ImageNet top-1 accuracy, while retaining compatibility with JPEG infrastructure. Our code is available at https://github.com/UT-SysML/seaotter .